Experimental Study on the Effects of Watermarking Techniques on EEG-Based Application System Performance

Watermarking has been suggested as a means to improve security of e-Health systems or to add additional functionalities to such system. All watermarking methods alter the host signal to some extent, though the acceptability of this modification varies with the watermarking scheme and depends on a particular application. However, the effect of watermarking methods on Electroencephalogram (EEG)-based applications has not been investigated. In this paper, we propose a robust EEG watermarking scheme and experimentally investigate the impact of applying the proposed method on the recognition performance of some EEG-based application systems such as emotion recognition and user authentication. We have found that the proposed EEG watermarking scheme results in a small degradation of performance.

[1]  Abdulhamit Subasi,et al.  EEG signal classification using wavelet feature extraction and a mixture of expert model , 2007, Expert Syst. Appl..

[2]  Ingemar J. Cox,et al.  Digital Watermarking and Steganography , 2014 .

[3]  José del R. Millán,et al.  Person Authentication Using Brainwaves (EEG) and Maximum A Posteriori Model Adaptation , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Leontios J. Hadjileontiadis,et al.  A Novel Emotion Elicitation Index Using Frontal Brain Asymmetry for Enhanced EEG-Based Emotion Recognition , 2011, IEEE Transactions on Information Technology in Biomedicine.

[5]  Ingemar J. Cox,et al.  Secure spread spectrum watermarking for multimedia , 1997, IEEE Trans. Image Process..

[6]  George N. Votsis,et al.  Emotion recognition in human-computer interaction , 2001, IEEE Signal Process. Mag..

[7]  Yongwha Chung,et al.  Analyzing the Secure and Energy Efficient Transmissions of Compressed Fingerprint Images using Encryption and Watermarking , 2008, 2008 International Conference on Information Security and Assurance (isa 2008).

[8]  John J. B. Allen,et al.  Frontal EEG asymmetry as a moderator and mediator of emotion , 2004, Biological Psychology.

[9]  Ayman Atia,et al.  Brain computer interfacing: Applications and challenges , 2015 .

[10]  Gregory W. Wornell,et al.  Quantization Index Modulation Methods for Digital Watermarking and Information Embedding of Multimedia , 2001, J. VLSI Signal Process..

[11]  Tien Pham,et al.  EEG-Based User Authentication in Multilevel Security Systems , 2013, ADMA.

[12]  Yen-Shou Lai,et al.  An SVD-based image watermarking in wavelet domain using SVR and PSO , 2012, Appl. Soft Comput..

[13]  Jana Dittmann,et al.  Digital watermarking of biometric speech references: impact to the EER system performance , 2007, Electronic Imaging.

[14]  Jing Dong,et al.  Effects of watermarking on iris recognition performance , 2008, 2008 10th International Conference on Control, Automation, Robotics and Vision.

[15]  Tieniu Tan,et al.  An SVD-based watermarking scheme for protecting rightful ownership , 2002, IEEE Trans. Multim..

[16]  Bin Hu,et al.  Towards affective learning with an EEG feedback approach , 2009, MTDL '09.

[17]  Alastair Allen,et al.  Effects of Reversible Watermarking on Iris Recognition Performance , 2014 .

[18]  Zhen Li,et al.  A Robust Audio Watermarking Scheme Based on Lifting Wavelet Transform and Singular Value Decomposition , 2011, IWDW.

[19]  Ingemar J. Cox,et al.  Review of watermarking and the importance of perceptual modeling , 1997, Electronic Imaging.

[20]  Gaurav Bhatnagar,et al.  Biometrics inspired watermarking based on a fractional dual tree complex wavelet transform , 2013, Future Gener. Comput. Syst..

[21]  Tien Pham,et al.  A Study on the Feasibility of Using EEG Signals for Authentication Purpose , 2013, ICONIP.

[22]  Kyandoghere Kyamakya,et al.  EEG-based emotion recognition approach for e-healthcare applications , 2016, ICUFN.

[23]  Daesung Moon,et al.  Performance Evaluation of Watermarking Techniques for Secure Multimodal Biometric Systems , 2005, CIS.

[24]  Mahmut Ozer,et al.  EEG signals classification using the K-means clustering and a multilayer perceptron neural network model , 2011, Expert Syst. Appl..

[25]  Chin-Chen Chang,et al.  SVD-based digital image watermarking scheme , 2005, Pattern Recognit. Lett..

[26]  Andreas Uhl,et al.  Experimental study on the impact of robust watermarking on iris recognition accuracy , 2010, SAC '10.

[27]  Wanli Ma,et al.  Enhancing Performance of EEG-based Emotion Recognition Systems Using Feature Smoothing , 2015, ICONIP.

[28]  Seyed Mojtaba Mousavi,et al.  Watermarking Techniques used in Medical Images: a Survey , 2014, Journal of Digital Imaging.

[29]  Punam Bedi,et al.  Optimized gray-scale image watermarking using DWT-SVD and Firefly Algorithm , 2014, Expert Syst. Appl..

[30]  Thierry Pun,et al.  DEAP: A Database for Emotion Analysis ;Using Physiological Signals , 2012, IEEE Transactions on Affective Computing.

[31]  R. Leeb,et al.  BCI Competition 2008 { Graz data set B , 2008 .